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Open AccessFeature PaperArticle

The Efficiency of Color Space Channels to Quantify Color and Color Intensity Change in Liquids, pH Strips, and Lateral Flow Assays with Smartphones

1
Institute for Global Food Security, School of Biological Sciences, Queen’s University of Belfast, 19 Chlorine Gardens, Belfast BT9 5DL, UK
2
Department of Food and Drug, University of Parma, Parco Area delle Scienze 27/A, 43124 Parma, Italy
3
School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, 125 Stranmillis Road, Belfast BT9 5AH, UK
4
Faculty of Chemistry, Lomonosov Moscow State University, 1-3 Leninskiye Gory, GSP-1, Moscow 119991, Russia
*
Authors to whom correspondence should be addressed.
Sensors 2019, 19(23), 5104; https://doi.org/10.3390/s19235104
Received: 25 October 2019 / Revised: 14 November 2019 / Accepted: 19 November 2019 / Published: 21 November 2019
(This article belongs to the Special Issue Advanced Sensors for Real-Time Monitoring Applications)
Bottom-up, end-user based feed, and food analysis through smartphone quantification of lateral flow assays (LFA) has the potential to cause a paradigm shift in testing capabilities. However, most developed devices do not test the presence of and implications of inter-phone variation. Much discussion remains regarding optimum color space for smartphone colorimetric analyses and, an in-depth comparison of color space performance is missing. Moreover, a light-shielding box is often used to avoid variations caused by background illumination while the use of such a bulky add-on may be avoidable through image background correction. Here, quantification performance of individual channels of RGB, HSV, and LAB color space and ΔRGB was determined for color and color intensity variation using pH strips, filter paper with dropped nanoparticles, and colored solutions. LAB and HSV color space channels never outperformed the best RGB channels in any test. Background correction avoided measurement variation if no direct sunlight was used and functioned more efficiently outside a light-shielding box (prediction errors < 5%/35% for color/color intensity change). The system was validated using various phones for quantification of major allergens (i.e., gluten in buffer, bovine milk in goat milk and goat cheese), and, pH in soil extracts with commercial pH strips and LFA. Inter-phone variation was significant for LFA quantification but low using pH strips (prediction errors < 10% for all six phones compared). Thus, assays based on color change hold the strongest promise for end-user adapted smartphone diagnostics. View Full-Text
Keywords: smartphone colorimetrics; lateral flow assay quantification; color space; image correction; food contaminant screening; allergens; background correction; point of site analyses smartphone colorimetrics; lateral flow assay quantification; color space; image correction; food contaminant screening; allergens; background correction; point of site analyses
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MDPI and ACS Style

Nelis, J.L.D.; Bura, L.; Zhao, Y.; Burkin, K.M.; Rafferty, K.; Elliott, C.T.; Campbell, K. The Efficiency of Color Space Channels to Quantify Color and Color Intensity Change in Liquids, pH Strips, and Lateral Flow Assays with Smartphones. Sensors 2019, 19, 5104.

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